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S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection
 

S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection

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    S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection Presentation Transcript

    • S-Cube Learning PackageA Soft-Constraint Based Approach to QoS-Aware Service Selection Université Paris-DESCARTES Mohamed-Anis ZEMNI, Salima BENBERNOU, Manuel CARRO www.s-cube-network.eu
    • Learning Package Categorization S-Cube Quality Definition, Negotiation and Assurance Quality Management and Prediction Analysis Operations on SLAs: Detecting and Explaining Conflicting SLAs
    • Service Selection and QoS Service selection is the first step to improve service composition within Service-Oriented-Architecture (SOA): •  Searches for services fitting users’ requirements •  Explores services’ properties •  Aims at putting together several elementary services •  Generates new value-added service Quality of Service (QoS) for selection often critically important: •  Software services expose not only functional characteristics, but also non-functional attributes describing their QoS •  Defines the service level (Key Performance Indicator) •  A service fulfilling all the functionality but with low QoS is not interesting
    • Learning Package Overview  Problem Description  Extending SCSP with Penalties & new SLA Model  Conclusions
    • Problem Description: Service Selection Scenario Select only one service among the available services that have the same functionalities but with different QoS Functionalities + QoSUser request (criteria) 1 2 Used Approach at Design-time
    • Problem Description:Service Selection Techniques in the Literature 1  Constraint Satisfaction Problem (CSP): •  Classical formulation of constraints •  Quite expressive to represent several real life problems •  Defines a set of variables, each of them ranging on a finite domain, and a set of constraints restricting the values that these variables can take simultaneously •  All the constraints must be satisfied simultaneously ! Lack of built-in capabilities to express preferences among constraints and the lack of possibility of giving approximate solutions for problems which are overconstrained
    • Problem Description:Service Selection Techniques in the Literature 1 Soft Constraint Satisfaction Problem (SCSP) •  Include the concept of preferences into every constraint in order to obtain a suitable solution which can be optimal or, in general, a reasonable estimation, maybe at the expense of not fulfilling all constraints •  Relies on composing the constraints in order to obtain the optimal solution •  Applied to the requirements (in terms of preferences) of the users ! Only one solution returned that is optimal * Stefano Bistarelli, Ugo Montanari, and Francesca Rossi. Semiring- based constraint satisfaction and optimization. J. ACM, 44(2):201– 236, 1997
    • Problem Description: Service Selection Techniques in the Literature 1 C-semi-ring : Algebraic structure Only one domain for all variablesExample : Searching for services Available at y% of the time and with reputation = z
    • Problem Description: Problem at Design-time 2.  I have to fix new criteria 1.  Required criteria cannot match any service!!!User request (criteria)
    • Problem Description:Problem at Runtime ! Some problems, encountered by the service may lead to service malfunctions activity interrupted, must apply penalty!!! Out of service Out of service contract violation
    • Problem Description:SLASLA - Definition: “An XML document and a contract for… •  Advertising the quality level of the services •  Taking note about the user preferences •  …” I want an SLA ensuring the performances I am searching for Propertie s Pro perties QoS ?
    • Problem Description: 2Problem at Runtime Where are My preferencesand the penalties? Out of service Out of service
    • Learning Package Overview  Problem Description  Extending SCSP with Penalties & new SLA Model  Conclusions
    • Main ObjectiveAutomatically switch from a faultyservice to a new one User request (preferences, … Out of service Out of penalties) service Design-time Runtime
    • Approach Main Points Definition of Soft Service Level Agreement (SSLA) an SLA model extended with preferences and penalties Extension of Soft Constraint Solving Problem handling penalties: Define in SSLA the penalty artifacts, such that, if a selected service failed, another one should replace it that fitting with the agreed QoS in the contract with penalties if some of them are not fulfilled SSLA to SCSP mapping
    • Kinds of penalties Arithmetical Penalties •  In relation with measurable qualities of service •  Direct relation to service variables •  E.g. availability, the response time, the reputation, etc. •  The application of arithmetical penalties is a consequence of a contract breach and therefore the transition to a different selection using the choices expressed by the customer in the form of preferences Behavioural Penalties •  Related to the behavior of either the customer or the service provider •  The application of behavioral penalties is not always a consequence of a contract breach and so, switching to another choice is not obligatory and even less replacing the service
    • Soft SLA Definition
    • Soft SLA Definition:Preferences & Penalties I prefer to get a payment service and delivery service having response time < 5ms. I also accept services with response time between 5ms and 20ms with preference =0,5 Etc. Response time Preferences If the first Most preferred preference is not <5ms fulfilled during the execution I would apply penalty P7 [5ms,20ms[ >20ms Less preferred
    • Soft SLA Definition Guarantee terms are expressed in terms of preferences and penalties •  Preferences are ranked (most preferred to less preferred) •  Penalties are applied if a preference is not fulfilled The service broker search for service fulfilling the QoS from the most preferred to the less preferred (at design-time) Penalties are applied only at runtime and never at design- time, on the faulty service SSLA document QoS Variable Preference Preferences Penalties Preferences/Penalties variables doamins degree association
    • Extending SCSP Using Penalties SCSP Constraint System Constraints Operations Solution
    • Extending Constraint System SCSP Constraint CS = <S; D{}; V> System S = algebraic structure including preference Constraints values V = QoS variables D{} = Variable domains Operations Penalties into S Solution
    • Extending Constraints Using Penalties SCSP Constraint Def = Definition of the System constraint in terms of preference value Constraints Type = in terms of variable intervening in the constraint Operations Penalties into Def Solution
    • Rewrite operations Logic SCSP Constraint System Combination = combination of the constraints (pref) Constraints Projection = generates the optimal solution Operations Rank generated solutions and keep them all Combination of penalties Solution
    • Extending SCSP Using Penalties SCSP Constraint System Global Preferences Constraints Most preferred + Operations Less preferred - Solution
    • Penalty based SCSPCase Study Penalty based SCSP Constraint System Constraints = Penalty values = Preference values Operations Solutions
    • Penalty based SCSPCase Study Penalty based SCSP Constraint System Constraints Operations Solutions
    • Penalty based SCSPCase Study Penalty based SCSP Constraint System Constraints Operations Solutions
    • Penalty based SCSPCase Study Penalty based SCSP Constraint System Constraints Operations Solutions
    • Proposed Approach LogicInput: Constraints, penalties, table of constraint definitionsOutput: Choices with their possible alternatives orderedBegin For each selection alternative do Combine all the constraints together (apply the min operator); End for; Order the results according to preference values into groups; For each preference value group do Order the elements corresponding to the penalty value; End for;End;
    • Mapping SSLA onto SCSP Solvers
    • Learning Package Overview  Problem Description  Extending SCSP with Penalties & new SLA Model  Conclusions
    • Conclusions1.  Soft constraint-based framework2.  Express QoS properties reflecting both customer preferences and penalties applied to unfitting situations3.  Solution for overconstrained problems –  The application of soft constraints makes it possible to work around overconstrained problems and offer a feasible solution4.  Provide ranked choice to offer more flexibility at design-time to find required services, and at runtime to ensure users’ rights5.  Concept of penalties in SCSP We plan to extend this framework to also deal with behavioral penalties
    • References This presentation is based on [ZBC10]
    • Further S-Cube Reading[ZBC10] Mohamed Anis Zemni, Salima Benbernou, and Manuel Carro A Soft Constraint-Based Approach to QoS-Aware Service Selection In proceeding of the Service-Oriented Computing - 8th International Conference (ICSOC 2010), volume 6470 of Lecture Notes in Computer Science, pages 596-602 San Francisco, CA, USA, December 7-10, 2010
    • Acknowledgements The research leading to these results has received funding from:   The European Community’s Seventh Framework Programme [FP7/2007-2013] under grant agreement 215483 (S-Cube).